Spring AI: Bridging Microservices and Large AI Models

Python has long dominated AI model training and development, with many mainstream frameworks such as PyTorch and TensorFlow implemented in Python or C++. This has made it challenging for traditional languages like Java to directly leverage the capabilities provided by AI models, lacking unified standards and often incurring higher development costs. However, as AI models have matured, their development, deployment, and service interfaces have gradually become standardized. In particular, recent advances in large models have further integrated and standardized the interaction between the application layer and AI service layer. This means that traditional developers can now call AI services and incorporate them into their applications as easily as they would with other third-party services, or even through remote AI service principles.

In this article, we introduce Spring AI from several perspectives:

Key concepts to know: MCP, RAG, LangChain — basic ideas to prepare for understanding Spring AI.
Spring AI overview: What it is, what it does, and how it fits into Spring microservices.
Deep dive into Spring AI: Core components, workflows, and Spring AI’s relationship with RAG.
Integration with Spring & microservices: How Spring AI components work together with traditional Spring layers, microservices, and cloud-native steps.

What is MCP?

Before we dive into Spring AI, it is crucial to understand MCP (Model Connection Protocol). MCP is a cross-language standardized communication protocol that defines how applications interact with AI services and models. It specifies how to pass context, structure requests and responses, and handle synchronous or asynchronous calls, ensuring that applications remain decoupled from specific AI providers.

Conceptually, MCP is similar to how gRPC, Thrift, or Protocol Buffers provide language-agnostic communication: any application, in any programming language, can implement or call MCP-compliant endpoints. In Java, Spring developers can think of it like how HttpUtil wraps HTTP protocols — applications do not need to handle low-level communication details directly, but rely on the standard protocol implementation.

MCP: Server, Host, and Client

To understand how MCP(Model Connection Protocol) works in practice, it is helpful to distinguish the three leading roles:

MCP Server

Responsibility: Implements the MCP protocol and exposes endpoints for applications to communicate with AI models. It handles incoming requests, manages context, and returns structured responses.
Example: A Java/Spring application could implement an MCP Server to serve AI workflows internally, or a Python-based microservice could expose MCP endpoints to orchestrate model calls.

MCP Host

Responsibility: The actual provider of the AI models or services. It may manage multiple AI engines, enforce quotas, handle token-based authentication, and execute model inference.
Example: OpenAI API, Hugging Face Inference API, Anthropic API-these are hosts that provide the underlying model capabilities. MCP standardizes the interaction between clients and servers and these hosts.

MCP Client

Responsibility: Sends requests to the MCP Server or Host following the MCP protocol, manages context, and optionally handles retries, caching, or aggregation of responses.
Example: Spring AI components act as MCP Clients, wrapping requests and responses in Spring-managed beans. A Golang/Python/Rust service could implement its own MCP client to communicate with the Same MCP Server or Host.

Explanation:

MCP Client: Communicates with the MCP Server using the standard protocol.
MCP Server: Orchestrates requests, caching, retries, and delegates work to one or more Hosts.
MCP Hosts: Runs the actual AI models and returns the request.

What is Spring AI?

For Spring microservices that function as AI service consumers, the primary focus is on utilizing AI services to manage application-level tasks such as request construction, response parsing, storage, and caching. This includes making efficient use of token resources and reusing responses when possible to minimize operational costs. Based on this, Spring AI is not an AI model itself, but rather a middleware layer that enables microservices to communicate with extensive AI models and services through standardized protocols, such as MCP. Its primary purposes include:

Allow developers to treat AI services like remote cloud services, serverless functions, or databases.
Provide Spring-native lifecycle management for AI-related components: creation, loading, containerization, and disposal.
Standardize AI service interaction to ensure consistency across teams and projects.
Support RAG workflows by combining retrieved external knowledge with AI model generation, enabling developers to implement retrieval-augmented generation patterns efficiently.

Essentially, Spring AI wraps all AI client SDKs and remote communication protocols into Spring-managed beans, providing a familiar lifecycle and DI integration for traditional Spring developers.

Because many AI models are trained in Python and deployed on remote large model service providers, Spring AI focuses on:

Standardized access to AI services via MCP.
Safe and controlled usage of remote models, including token management, request throttling, and response reuse.
Template-based integration into microservices, ensuring uniformity while maintaining flexibility where necessary.

What’s RAG?

RAG (Retrieval-Augmented Generation) is a technique that enhances AI models by combining retrieved external knowledge with generative models. Traditional large language models can struggle with factual accuracy, long contexts, or domain-specific knowledge. RAG addresses these challenges by:

Retrieving relevant data from external sources (databases, document stores, knowledge bases)
Augmenting the model input with this retrieved information.
Generating responses that are more accurate, context-aware, and grounded in the external knowledge.

In the context of Spring AI:

Spring AI can orchestrate RAG workflows by integrating retrieval logic and model calls within the application layer.
Microservices can use Spring AI to handle request building, response parsing, caching, and RAG-enhanced generation, all without dealing with low-level API calls to each AI provider.
RAG is an application-layer pattern, not a model itself. Developers may optionally fine-tune smaller domain-specific models for enhanced performance, but the core of RAG is the retrieval + generation process.

By combining MCP’s standardized communication protocol, Spring AI’s abstractions, and RAG workflows, enterprise applications can deploy complex AI capabilities in a structured, maintainable, and Spring-native way. Additionally, leveraging Spring’s cloud-native strengths allows the entire workflow — from request orchestration and model access to retrieval and response handling — to be fully cloud-native, enabling scalable, resilient, and easily deployable AI services across distributed environments.

Where Does LangChain Fit?

When talking about RAG, we often hear LangChain mentioned. LangChain is a Python-based framework that focuses on orchestrating multi-step LLM workflows, including RAG pipelines. Its role is not to provide the model itself, but to manage:

Retrieval orchestration (connecting to vector stores, pulling relevant data)
Prompt construction (structuring the query with retrieved data)
Chaining tasks (multi-step reasoning with memory and agents)

In this sense:

LangChain = workflow orchestrator (Python ecosystem)
MCP = protocol (cross-language, defines how clients/servers/hosts talk)
Spring AI = Java/Spring abstraction over MCP + RAG, making it easy for Spring microservices to plug into the ecosystem.

So Spring AI plays a similar role for Java developers as LangChain does for Python developers, but with strong integration into the Spring Boot and Spring Cloud ecosystem.

MCP = protocol for communicating with AI Hosts across language and providers

Spring AI = Java/Spring-native framework that enables similar RAG workflows

RAG = technique/pattern (retrieve + generate)

Deep Dive into Spring AI

Now, let’s deep dive into Spring AI and explore its core components, along with the responsibilities they assume when integrating AI capabilities into enterprise applications. In this section, we will look at:

The essential components of Spring AI
How these components integrate with traditional microservice layers
The key stages of a Spring AI workflow
The additional DevOps and deployment considerations when operating AI-powered services at scale

Essential Components

Spring AI introduces a set of abstractions that map naturally onto familiar Spring layers:

Integration with Traditional Microservices

In a typical Spring microservice, the layers evolve as follows when AI is introduced:

Controller Layer: Handles REST/GraphQL API calls, exposing AI-enhanced services.
Service Layer: Business logic now includes the orchestration of AI workflows (AIService, Retriever, and PromptTemplate).
Data Layer: Traditional relational/NoSQL databases are extended with VectorStores for embeddings and similarity search.
Integration Layer: AIClient abstracts communication with remote AI providers via MCP or direct APIs.

Thus, AI capabilities integrate seamlessly with existing service components, without breaking the established Spring architecture.

Spring AI Workflow Stages

When integrating Spring AI into a Spring microservice, the request-response flow gains a few additional steps specific to AI processing. These include fetching relevant context, constructing prompts, calling AI models, parsing responses, and optionally caching or persisting results. Understanding this workflow helps developers see how AI features fit into familiar service patterns and what adjustments are needed in daily development.

A typical Spring AI workflow involves several critical steps:

Request Handling: An incoming request reaches an AI Controller.

Context Retrieval (Optional, RAG): A Retriever fetches relevant documents from a VectorStore.

Prompt Construction: A PromptTemplate combines the request input with the retrieved context.

Model Invocation: The AIClient sends the prompt to the AI provider (via MCP or direct API).

Response Parsing: The response is normalized and transformed into application-level objects.

Caching/Persistence: Token usage is optimized by caching the response or persisting the results in a DB.

Delivery: The enriched response is returned to the client or forwarded to downstream services.

Integrating Spring AI into the Daily Routines of Traditional Spring Developers

Introducing AI into your microservices doesn’t mean reinventing your workflow. From a Java developer’s perspective, Spring AI can be integrated into familiar patterns while also leveraging cloud-native practices:

API Keys & Secret Management: Securely store AI provider credentials (OpenAI tokens, Hugging Face keys) in Vault, KMS, or Spring Config Server — just like any other sensitive configuration.
Rate Limiting & Quotas: Control usage of AI services to avoid overages, applying patterns similar to throttling external APIs.
Observability: Monitor AI request latency, token usage, and errors with the same logging and metrics tools you already use in Spring.
Caching Layers: Reuse AI responses or intermediate results with Redis or other caches to optimize performance and cost.
Vector Store Deployment: Manage embeddings for RAG workflows, integrating vector stores like Milvus or Postgres + pgvector into your existing data layer.
Resiliency Patterns: Apply retries, circuit breakers, and bulkheads (via Spring Cloud Resilience4j) to ensure reliable AI service calls.
CI/CD & Cloud-Native Scaling: Package AI-enabled services as containers, deploy on Kubernetes, and autoscale based on request volume and token usage — leveraging your existing cloud-native knowledge.

By treating Spring AI as another Spring-managed service, developers can adopt AI features without disrupting their daily routines, while still benefiting from standardized workflows, RAG patterns, and cloud-native best practices.

Summary

In summary, Spring AI extends the Spring programming model into the AI domain. Its components align naturally with existing microservice layers, while adding new workflow stages (retrieval, prompt building, embedding storage) and fresh DevOps considerations (token monitoring, vector databases, observability).

Although AI is becoming a more mature and industrialized technology, when learning and experimenting with it — especially as experienced software engineers — it’s essential to approach it from the perspective of our existing knowledge base. This allows us to explore AI through a familiar lens, incorporate it into a business context, and adapt more quickly to AI design and development practices. AI itself isn’t entirely new, but understanding how to integrate it into our daily development workflows — including testing, deployment, feature validation, and working with large-scale DevOps or cloud-native practices — is essential for becoming proficient with it.

This article doesn’t try to cover every aspect of AI, but it offers a unique perspective and a solid starting point. From this foundation, developers can analyze AI components, see how they fit with traditional development workflows, and create enterprise-grade, maintainable AI-powered applications — progressing from toy projects to production-ready solutions.

Ace Your Next Profession Level 🚀

Are you preparing for a Java/Spring interview or aiming to level up with a Spring Professional Certification? Whether you’re doing last-minute prep or working toward mastery, these resources are designed to help you succeed:

🔹 Grokking the Java Interview — Master the most common Java interview questions with clear explanations and examples.

🔹 Grokking the Spring Boot Interview — Get interview-ready with Spring Boot insights every professional should know.

🔹 250+ Spring Professional Certification Practice Questions — Build confidence with real exam-style practice questions to boost your certification prep.

Free Samples Available! Try before you commit ✨:

Perfect for 🎯:

Java developers preparing for interviews
Spring Boot professionals aiming to stand out
Engineers leveling up for certification exams

💡 Gain the edge you need to advance to the next level in your career. These are some of the top resources available for focused and structured preparation.

Spring AI: Bridging Microservices and Large AI Models was originally published in Javarevisited on Medium, where people are continuing the conversation by highlighting and responding to this story.

This post first appeared on Read More